MIT CSAIL - Edward H. Adelsonhttp://www.csail.mit.edu/taxonomy/term/64/0
enApparent Ridges for Line Drawinghttp://www.csail.mit.edu/videoarchive/research/gv/apparent-ridges
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<a href="http://projects.csail.mit.edu/video/research/gv/sig07_tjudd.mp4" target="_blank" rel="nofollow">http://projects.csail.mit.edu/video/research/gv/sig07_tjudd.mp4</a> </div>
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http://projects.csail.mit.edu/video/thumbs/apparent-ridges.png </div>
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Three-dimensional shape can be drawn using a variety of feature lines, but none of the current definitions alone seem to capture all visually-relevant lines. We introduce a new definition of feature lines based on two perceptual observations. First, human perception is sensitive to the variation of shading, and since shape perception is little affected by lighting and reflectance modification, we should focus on normal variation. Second, view-dependent lines better convey smooth surfaces. From this we define view-dependent curvature as the variation of the surface normal with respect to a viewing screen plane, and apparent ridges as the loci of points that maximize a view-dependent curvature. We present a formal definition of apparent ridges and an algorithm to render line drawings of 3D meshes. We show that our apparent ridges encompass or enhance aspects of several other feature lines. </div>
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Graphics and VisionEdward H. AdelsonFredo Durandgraphicssiggraph07Tilke JuddTue, 30 Jun 2009 20:24:01 +0000Tom Buehler723 at http://www.csail.mit.eduMotion Magnificationhttp://www.csail.mit.edu/videoarchive/research/gv/motion-magnification
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<a href="http://projects.csail.mit.edu/video/research/gv/celiu_siggraph_2005.mp4" target="_blank" rel="nofollow">http://projects.csail.mit.edu/video/research/gv/celiu_siggraph_2005.mp4</a> </div>
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http://projects.csail.mit.edu/video/thumbs/motion-magnify.png </div>
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We present motion magnification, a technique that acts like a microscope for visual motion. It can amplify subtle motions in a video sequence, allowing for visualization of deformations that would otherwise be invisible. To achieve motion magnification, we need to accurately measure visual motions, and group the pixels to be modified. After an initial image registration step, we measure motion by a robust analysis of feature point trajectories, and segment pixels based on similarity of position, color, and motion. A novel measure of motion similarity groups even very small motions according to correlation over time, which often relates to physical cause. An outlier mask marks observations not explained by our layered motion model, and those pixels are simply reproduced on the output from the original registered observations. The motion of any selected layer may be magnified by a user-specified amount; texture synthesis fills-in unseen holes revealed by the amplified motions. The resulting motion-magnified images can reveal or emphasize small motions in the original sequence, as we demonstrate with deformations in load-bearing structures, subtle motions or balancing corrections of people, and rigid structures bending under hand pressure. </div>
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Graphics and VisionAntonio TorralbaCe LiuEdward H. AdelsonFredo Durandsiggraph05visionWilliam T. FreemanMon, 29 Jun 2009 20:52:33 +0000Tom Buehler703 at http://www.csail.mit.edu